Dynamic balance control method for new energy automobile battery pack

Abstract

The invention discloses a dynamic balance control method for a new energy automobile battery pack, and relates to the technical field of battery control, and the method comprises the steps: obtaining N battery packs of a new energy automobile; setting a plurality of characteristic working condition modes of the new energy automobile; collecting a historical operation condition data set of the N battery packs; carrying out battery load calculation, outputting N load index samples corresponding to different characteristic working condition modes, and constructing a balance control grouping model; and identifying a real-time working condition mode of the new energy vehicle, outputting the balance control battery pack divided in the real-time working condition mode, and carrying out load balance control. The technical problems of low flexibility, low efficiency and short service life during load control of the battery pack due to lack of grouping of the battery pack in different scenes in the prior art are solved, accurate load balance control of the battery pack of the new energy automobile is realized, and the service life of the battery pack is prolonged. And the overall efficiency and the service life of the battery pack are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery control, and particularly to a dynamic balance control method for a battery pack of a new energy vehicle. Background Art

[0002] With the rapid development of new energy vehicles, the battery pack, as the core component of new energy vehicles, its performance and lifespan directly affect the overall performance of new energy vehicles. However, in the prior art, there is often a lack of effective load control for the battery pack. Since new energy vehicles will experience different modes during driving, the usage frequency and energy consumption of each battery pack also vary. If precise load balance control cannot be performed on the battery pack, it will lead to overuse or excessive load of some battery cells, thereby reducing the overall efficiency and lifespan of the battery pack.

[0003] The prior art has the technical problem that the lack of grouping of the battery pack in different scenarios results in low flexibility, low efficiency, and short lifespan during load control of the battery pack. Summary of the Invention

[0004] The present application provides a dynamic balance control method for a battery pack of a new energy vehicle, which is used to solve the technical problem in the prior art that the lack of grouping of the battery pack in different scenarios results in low flexibility, low efficiency, and short lifespan during load control of the battery pack.

[0005] In view of the above problems, the present application provides a dynamic balance control method for a battery pack of a new energy vehicle, and the method includes: Obtain N battery packs of the new energy vehicle; set multiple characteristic working condition modes of the new energy vehicle, and the multiple characteristic working condition modes include an acceleration working condition mode, a deceleration working condition mode, a climbing working condition mode, and a braking working condition mode; collect a historical operation working condition data set of the N battery packs under different characteristic working condition modes; perform battery load calculation on the historical operation working condition data set, output N load index samples corresponding to the N battery packs under different characteristic working condition modes, and construct a balance control grouping model; identify the real-time working condition mode of the new energy vehicle, and based on the balance control grouping model, output the balance control battery packs divided under the real-time working condition mode, and perform load balance control on the balance control battery packs.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages: Obtain N battery packs of a new energy vehicle; set multiple characteristic working condition modes of the new energy vehicle; collect a historical operation working condition data set of the N battery packs under different characteristic working condition modes; perform battery load calculation on the historical operation working condition data set, output N load index samples, and construct a balance control grouping model; identify the real-time working condition mode of the new energy vehicle, output a balance control battery pack, and perform load balance control. The technical effect of achieving precise load balance control of the battery packs of a new energy vehicle and improving the overall efficiency and lifespan of the battery packs is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0008] Figure 1 It is a schematic flowchart of a dynamic balance control method for a battery pack of a new energy vehicle provided by an embodiment of the present application; Figure 2 It is a schematic flowchart of constructing a balance control grouping model in a dynamic balance control method for a battery pack of a new energy vehicle provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0009] The present application provides a dynamic balance control method for a battery pack of a new energy vehicle to solve the technical problem in the prior art that there is a lack of grouping of battery packs in different scenarios, resulting in low flexibility, low efficiency, and short service life during load control of battery packs.

[0010] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0011] Embodiment, as Figure 1 shown, the present application provides a dynamic balance control method for a battery pack of a new energy vehicle, and the method includes: Step S100: Obtain N battery packs of a new energy vehicle.

[0012] Specifically, obtaining N battery packs of a new energy vehicle is the starting step of the entire dynamic balance control method. This step requires clearly identifying and obtaining all the battery packs involved in electric energy storage and supply in the vehicle. First, determine the battery system architecture of the new energy vehicle, understand how the battery packs are distributed and connected, consult the electrical system design documents of the vehicle, and obtain relevant information through the vehicle's diagnostic interface. Then, according to the determined distribution of the battery packs, use monitoring equipment to obtain relevant data of each battery pack. The monitoring equipment can accurately measure key parameters such as the voltage, current, and temperature of the battery pack, so as to evaluate and analyze the performance of the battery pack in the subsequent steps. At the same time, number or label the obtained battery packs so that each battery pack can be accurately identified and distinguished in the subsequent steps. Through these operations, ensure that N battery packs of the new energy vehicle can be obtained accurately and error-free, providing a basic data source for subsequent steps such as operating condition mode setting, data acquisition, and balance control.

[0013] Step S200: Set multiple characteristic operating condition modes of the new energy vehicle, where the multiple characteristic operating condition modes include an acceleration operating condition mode, a deceleration operating condition mode, a climbing operating condition mode, and a braking operating condition mode.

[0014] Specifically, multiple characteristic operating condition modes are set for new energy vehicles, which are crucial for accurately analyzing the operating conditions of the battery pack. First is the acceleration operating condition mode, which simulates the state of the vehicle during acceleration. In this mode, the vehicle requires a large power output, and the battery pack needs to quickly release electrical energy to meet the requirements of the motor. At this time, the current output of the battery pack is large, the voltage will fluctuate to a certain extent, and the temperature of the battery also rises due to high load. The deceleration operating condition mode is the situation when the vehicle decelerates. When the vehicle decelerates, the motor switches to the generator mode to charge the battery pack. The battery pack will receive electrical energy in this mode, and its charging current and voltage also need to be monitored and analyzed because different deceleration situations may lead to differences in the charging process. The climbing operating condition mode is set for the scenario of the vehicle climbing a slope. When climbing a slope, the vehicle needs to overcome gravity to do work and has a large demand for power. The battery pack needs to continuously and stably provide electrical energy, which requires the battery pack to maintain a high output power under this operating condition and also consider the impact of complex road conditions that may occur during the climbing process on the performance of the battery pack, such as frequent starts and stops and load changes. Finally is the braking operating condition mode, which is similar to the deceleration operating condition mode but focuses more on the situation when the vehicle brakes. During the braking process, in addition to the possible charging effect of the motor, it also involves the energy recovery and collaborative working mechanism between the vehicle braking system and the battery pack, and it is necessary to pay attention to the charging efficiency of the battery pack during braking and its impact on the overall braking performance of the vehicle. By setting these four characteristic operating condition modes, namely the acceleration operating condition mode, the deceleration operating condition mode, the climbing operating condition mode, and the braking operating condition mode, all typical operating states of new energy vehicles during actual driving are comprehensively covered, providing an accurate basis for classifying operating conditions for subsequent collection of the historical operating condition dataset of the battery pack, and thus contributing to the construction of a reasonable balance control grouping model.

[0015] Step S300: Collect the historical operating condition dataset of the N battery packs under different characteristic operating condition modes.

[0016] Specifically, for the acceleration condition mode, professional monitoring equipment is required to collect data. These devices should be able to accurately measure parameters such as the voltage, current, temperature, and state of charge (SOC) of the battery pack. During acceleration, the current of the battery pack will rise rapidly, the voltage may fluctuate, and the temperature may increase due to high load. Therefore, data collection should be carried out at multiple time points during acceleration to ensure obtaining the complete operating data of the battery pack under the acceleration condition. In the deceleration condition mode, relevant data should also be collected using monitoring equipment. When the vehicle decelerates, the motor may switch to the generator mode to charge the battery pack. At this time, key attention should be paid to data such as the charging current, charging voltage, and temperature change during the charging process of the battery pack, and the charging conditions of the battery pack under different deceleration degrees should be collected to comprehensively understand the operating conditions of the battery pack in the deceleration condition mode. In the climbing condition mode, the monitoring equipment should collect data such as the output power, voltage stability, temperature change, and state of charge (SOC) of the battery pack during climbing. Since the vehicle has a continuous demand for power during climbing, data collection should be carried out at different stages of climbing, such as the initial stage, middle stage, and late stage of climbing, to obtain a comprehensive operating data set of the battery pack under the climbing condition. For the braking condition mode, in addition to collecting data related to the charging of the battery pack, the impact of the energy recovery mechanism between the braking system and the battery pack on the operation of the battery pack should also be considered. Data such as the charging current, charging voltage, temperature change, and energy recovery efficiency of the battery pack during braking should be collected, and these data should reflect the operating conditions of the battery pack under different braking intensities. By collecting various parameters of N battery packs under different characteristic condition modes, a comprehensive historical operating condition data set is finally obtained, providing a solid data basis for subsequent battery load calculation and the construction of the balance control grouping model.

[0017] Step S400: Calculate the battery loads for the historical operating condition data set, output N load index samples corresponding to the N battery packs under different characteristic condition modes, and construct a balance control grouping model.

[0018] Specifically, first, the historical operating condition dataset covers rich information on new energy vehicles in various past actual usage scenarios. Under different working conditions, such as during acceleration, the vehicle requires a greater power output, the discharge current of the battery pack increases, and the loads borne by battery cells at different positions will increase accordingly. During deceleration, there is energy recovery, and the load conditions of the battery cells are different. During the climbing process, due to the need to continuously output a relatively large power, the battery cells face a high load. And during braking, energy is fed back to the battery pack. Then, battery load calculation is carried out. By analyzing parameters such as vehicle speed, acceleration, motor output power, and the charge and discharge states of the battery pack in the dataset, and comprehensively considering factors such as current, voltage, temperature changes, and state of charge changes, the load indicators of each battery cell in different characteristic operating condition modes are accurately calculated. For example, the power is calculated based on the discharge current and voltage under different operating conditions, and the energy consumption is obtained by combining the time factor. At the same time, the influence of temperature changes on battery performance is considered to determine the load indicators. After outputting N load indicator samples corresponding to N battery cells in different characteristic operating condition modes, the specific load performance of each battery cell under various operating conditions can be deeply understood. Based on these sample data, the usage probability of each battery cell under different operating conditions can be further evaluated. If a certain battery cell often bears a high load during the acceleration condition, then its usage probability during the acceleration state is relatively high. By analyzing these usage probabilities, the balance control strategy of the battery management system can be optimized more pertinently. When constructing the balance control grouping model, according to the load indicators and usage probabilities of different battery cells, they are reasonably divided into different control groups. For battery cells with a high load and a large usage frequency, the load balance adjustment is carried out more frequently to ensure their stable performance. For battery cells with a relatively low overall load, a relatively loose control strategy is adopted. In this way, the overall efficiency and lifespan of the battery pack can be effectively ensured, and the battery management system of the new energy vehicle can be made more intelligent and efficient.

[0019] Step S500: Identify the real-time operating condition mode of the new energy vehicle, and based on the balance control grouping model, output the balanced control battery pack divided in the real-time operating condition mode, and perform load balance control in the balanced control battery pack.

[0020] Specifically, it mainly includes three key operations: identifying the real-time working condition mode, determining the balanced control battery pack, and performing load balancing control. First, it is to identify the real-time working condition mode of new energy vehicles, which requires a comprehensive judgment of the current operating state of the vehicle. By collecting relevant data of the vehicle, such as vehicle speed, acceleration, motor torque, charge and discharge current and voltage of the battery pack, etc., data analysis algorithms are used to determine what working condition mode the vehicle is currently in. For example, if the vehicle speed increases rapidly in a short period of time, and the battery pack is in a discharging state with a large discharging current, combined with other parameters such as acceleration, it is judged that the vehicle is in an accelerating working condition mode. If the vehicle speed gradually decreases, the direction of the motor torque changes and the battery pack starts to charge, then it is in a decelerating working condition mode. For the climbing working condition mode, it is judged by the vehicle's tilt angle sensor, continuous large discharging current of the battery pack and specific vehicle speed change patterns, etc. The braking working condition mode is determined by the signal of the brake pedal, the charging condition of the battery pack and relevant vehicle dynamics parameters. Next, based on the balanced control grouping model, the balanced control battery pack divided under the real-time working condition mode is output. When the real-time working condition mode is determined, the balanced control grouping model will operate according to the pre-set rules and strategies. For each working condition mode, the model has corresponding balanced control grouping strategies. For example, in the accelerating working condition mode, the model will, according to the previously constructed rules, divide the eligible battery packs into the passive balance control group and the active balance control group. These conditions are determined based on the analysis results of the battery pack load index samples under different characteristic working condition modes. If the load index of a certain battery pack is within a specific range in the accelerating working condition mode, it will be divided into the passive balance control group; if it is in other ranges, it will be divided into the active balance control group. Similarly, in other working condition modes, such as decelerating, climbing and braking working condition modes, the model will also divide the battery packs according to their respective rules. Finally, load balancing control is performed on the balanced control battery pack. For the battery packs divided into the passive balance control group, load balancing will be carried out according to the passive balance control strategy. For example, a passive balance module is configured for the battery packs in the passive balance control group. This module includes energy-consuming components (such as variable resistors) and MOSFETs. The control parameters of the energy-consuming components, such as resistance rate and resistance duration, are obtained through a proportional control algorithm based on the load difference to adjust the load of the battery pack to make it balanced. For the battery packs divided into the active balance control group, load balancing will be carried out according to the active balance control strategy. For example, an active balance module (such as a bidirectional DC-DC converter) is configured for these battery packs. The control parameters of the bidirectional DC-DC converter, such as energy transfer direction, energy transfer time and energy transfer rate, are obtained based on the proportional control algorithm of the load difference, so as to realize the balanced control of the battery pack load. In this way, effective load balancing control of the battery pack can be carried out under different real-time working condition modes, improving the overall performance and service life of the battery pack of new energy vehicles.

[0021] In a possible implementation, as Figure 2 shown, step S400 further includes: Step S410: Analyze the N load index samples corresponding to the N battery packs under each characteristic operating condition mode, and identify the first type of battery packs and the second type of battery packs under each characteristic operating condition mode.

[0022] Step S420: Generate a passive balance control group with the first type of battery packs, generate an active balance control group with the second type of battery packs, and obtain the balance control grouping strategy under each characteristic operating condition mode.

[0023] Step S430: Construct the balance control grouping model according to the balance control grouping strategy under each characteristic operating condition mode.

[0024] Specifically, first, a reasonable classification standard needs to be set according to the performance characteristics of the battery packs and the actual application requirements. Divide according to the magnitude of the load index, determine that the first preset load index value is P1 and the second preset load index value is P2, and P1 < P2. Analyze one by one the N load index samples corresponding to the N battery packs under each characteristic operating condition mode. For example, in the acceleration operating condition mode, analyze the load index samples of each battery pack, considering the load conditions it bears during acceleration, including the comprehensive effects of factors such as current, voltage, temperature, and SOC on the load. Similarly, in the deceleration, climbing, and braking operating condition modes, also conduct a detailed analysis of the load index samples of the battery packs under their respective operating conditions to understand the load characteristics of each battery pack under the corresponding operating conditions. According to the set classification standard, identify the first type of battery packs and the second type of battery packs under each characteristic operating condition mode. The first type of battery packs refers to the battery packs whose load index P1 is greater than and less than P2. The load of these battery packs is in a relatively moderate range under the corresponding operating conditions. The second type of battery packs refers to the battery packs whose load index is less than or equal to P1 or greater than or equal to P2. The load of these battery packs is either too low or too high under the corresponding operating conditions.

[0025] Clarify the classification criteria for the first type of battery packs and the second type of battery packs. The first type of battery packs are those with a load index greater than the first preset load index and less than the second preset load index; the second type of battery packs are those with a load index less than or equal to the first preset load index and greater than or equal to the second preset load index, and the first preset load index is less than the second preset load index. For the first type of battery packs, generate a passive balance control group. Passive balance control uses energy-consuming components (such as variable resistors) to consume excess energy to achieve load balance. The load of these battery packs is in a relatively moderate range and does not require overly complex control strategies. The passive balance method can meet their load balance requirements to a certain extent.

[0026] For the second type of battery pack, an active balance control group is generated. Since the load indicators of this type of battery pack are either too high or too low, a more proactive control method is required to achieve load balance. A bidirectional DC-DC converter is used to transfer and adjust energy, enabling the load of the battery pack to reach a balanced state. Active balance control can more precisely adjust the load of the battery pack to adapt to different working condition requirements. After generating the passive balance control group and the active balance control group, the balance control grouping strategy for each characteristic working condition mode is further obtained. Different characteristic working condition modes, such as the acceleration working condition mode, deceleration working condition mode, climbing working condition mode, and braking working condition mode, have different load requirements for the battery pack. Therefore, specific balance control grouping strategies need to be formulated for each working condition mode. For example, in the acceleration working condition mode, according to the load change of the battery pack, the division criteria of the passive balance control group and the active balance control group are dynamically adjusted, and the specific control parameters of different control groups are determined. For the passive balance control group, parameters such as the resistance rate and resistance duration of the variable resistor are adjusted according to the load increase during the acceleration process. For the active balance control group, parameters such as the energy transfer direction, energy transfer time, and energy transfer rate of the bidirectional DC-DC converter are determined according to the load level. Provide an effective load balance control strategy for the battery pack of new energy vehicles under different characteristic working condition modes, and improve the overall performance and lifespan of the battery pack.

[0027] Construct a balance control grouping model according to the balance control grouping strategy for each characteristic working condition mode. This model is a rule-based model that determines the control group (passive balance control group or active balance control group) to which the battery pack belongs based on the real-time working condition mode and the load indicator of the battery pack, and performs load balance control according to the corresponding control strategy. For example, if the real-time working condition mode is the acceleration working condition mode, when the load indicator of a battery pack is within the range of the first type of battery pack, the model classifies it into the passive balance control group and performs load balance control according to the strategy of the passive balance control group; when the load indicator is within the range of the second type of battery pack, the model further classifies it into the active balance control group and performs load balance control according to the strategy of the active balance control group. For the deceleration working condition mode, when the load indicator of the battery pack meets the corresponding classification criteria, the model accurately classifies it into the passive balance control group or the active balance control group and implements load balance control according to their respective control strategies. In the climbing working condition mode, also based on the load indicator and classification criteria, the model divides the battery pack into the appropriate control group and then balances the load according to the corresponding control strategy. For the braking working condition mode, the model correctly assigns the battery pack to the passive balance control group or the active balance control group according to the load indicator of the battery pack, and then realizes load balance control through the corresponding control strategy.

[0028] In a possible implementation manner, step S410 further includes: Step S411: The first type of battery pack is a battery pack with a load index greater than a first preset load index and less than a second preset load index, and the second type of battery pack is a battery pack with a load index less than or equal to the first preset load index and greater than or equal to the second preset load index, where the first preset load index is less than the second preset load index.

[0029] Specifically, the first type of battery pack and the second type of battery pack are set according to different ranges of the load index. For the first type of battery pack, the range of its load index is greater than the first preset load index and less than the second preset load index. The first preset load index and the second preset load index are determined based on the actual operating characteristics, performance requirements of the battery pack, as well as past experimental data and experience. The first preset load index is relatively small, and the second preset load index is relatively large. When the load index of the battery pack is within this range, it indicates that the load of the battery pack under the corresponding working conditions is in a relatively moderate state. For example, under the acceleration working condition, under the comprehensive influence of factors such as current, voltage, temperature, and SOC, the load index of the first type of battery pack conforms to this moderate range, neither being too high to cause excessive battery loss nor being too low to affect the vehicle's power output.

[0030] The second type of battery pack includes battery packs with a load index less than or equal to the first preset load index and greater than or equal to the second preset load index. This means that the load of this type of battery pack is in two extreme situations. On the one hand, the battery pack with a load index less than or equal to the first preset load index is in a low-load state under certain working conditions. For example, during vehicle coasting or energy recovery, the discharge of these battery packs is small or they are in a charging state, and the load is relatively low. On the other hand, the battery pack with a load index greater than or equal to the second preset load index bears a relatively high load pressure under high-power output working conditions, such as sudden acceleration and climbing. Through such a clear classification, different balance control strategies are adopted for different types of battery packs. For the first type of battery pack, a relatively simple passive balance control method is adopted to achieve load balance by consuming a small amount of excess energy. For the second type of battery pack, due to its relatively complex load situation, a more active and precise control method is adopted, using an active balance control strategy such as energy transfer with a bidirectional DC-DC converter to ensure that the battery pack can maintain good performance and lifespan under various working conditions.

[0031] In a possible implementation manner, step S500 further includes: Step S510: The balance control grouping model matches the real-time working condition mode with the multiple characteristic working condition modes to obtain the balance control grouping strategy under the real-time working condition mode.

[0032] Step S520: Output the passive balance control group and the active balance control group divided under the real-time operating condition mode according to the balance control grouping strategy.

[0033] Step S530: Record the real-time operating condition data set of the new energy vehicle under the real-time operating condition mode.

[0034] Step S540: Perform balance identification according to the real-time operating condition data set, output the passive balance control parameters and the active balance control parameters, and perform load balance control on the passive balance control group and the active balance control group respectively with the passive balance control parameters and the active balance control parameters.

[0035] Specifically, the balance control grouping model matches the real-time operating condition mode with multiple characteristic operating condition modes through the decision tree algorithm, and then obtains the balance control grouping strategy under the real-time operating condition mode. First, collect multi-faceted data on the current operating state of the vehicle, including vehicle speed, acceleration, motor torque, charge and discharge current and voltage of the battery pack, etc. These original data will first go through preprocessing. During the preprocessing, the data cleaning step will remove outliers that do not conform to the normal operating rules of the vehicle. For example, if the vehicle speed suddenly has a very large value and does not conform to the vehicle acceleration or deceleration logic, it will be corrected or removed. At the same time, the data normalization operation will convert data with different ranges and units into a unified numerical interval for better processing by subsequent algorithms. Then, extract key features from the preprocessed data. For the acceleration operating condition mode, the extracted features include a relatively high acceleration value (acceleration greater than the set acceleration threshold), a relatively large discharge current of the battery pack (discharge current greater than a certain set discharge current threshold), and a rapid increase trend of the vehicle speed, etc. For the deceleration operating condition mode, the extracted features are a negative acceleration value (acceleration less than the set deceleration threshold), the appearance of the battery pack charging current, and a decreasing trend of the vehicle speed, etc. For the climbing operating condition mode, in addition to the relatively large and continuous discharge current of the battery pack, it also includes features such as the vehicle tilt angle. For the braking operating condition mode, the extracted features include the signal strength of the brake pedal, the battery pack charging current, and a rapid decreasing trend of the vehicle speed, etc. Then, use these extracted features as the input nodes of the decision tree algorithm. Each node of the decision tree branches according to different feature values. For example, the root node branches according to the acceleration value. If the acceleration is greater than a certain acceleration threshold, it enters the sub-branch related to the acceleration operating condition mode; if the acceleration is less than a certain deceleration threshold, it enters the sub-branch related to the deceleration operating condition mode. In the sub-branch related to the acceleration operating condition mode, it further branches according to the battery pack discharge current. If the discharge current is greater than a certain discharge current threshold and the vehicle speed is increasing rapidly, it is finally determined as the acceleration operating condition mode. Once it is determined through the decision tree algorithm that the real-time operating condition mode matches a certain characteristic operating condition mode successfully, the model can obtain the balance control grouping strategy under this real-time operating condition mode. This strategy is preset and optimized for each characteristic operating condition mode during the construction of the balance control grouping model. It details how to group and control the battery pack under this operating condition mode, including determining which battery packs should be divided into the passive balance control group, which should be divided into the active balance control group, and the specific control methods and parameter settings for different groups, etc. For example, the balance control grouping strategy under the acceleration operating condition mode may stipulate that battery packs with load indicators in a certain specific interval are divided into the passive balance control group and load balance control is carried out in a certain way (such as series variable resistors); while battery packs with load indicators in other intervals are divided into the active balance control group and load balance control is carried out in another way (bidirectional DC-DC converter).

[0036] According to the obtained balance control grouping strategy, the battery pack under the real-time working condition mode is divided. First, clarify the division criteria specified in this strategy. Based on factors such as the load index range of the battery pack, for the battery packs that meet the division criteria of the passive balance control group, they are screened out from all the battery packs to form the passive balance control group. At the same time, for the battery packs that meet the division criteria of the active balance control group, they are also screened according to the corresponding rules to form the active balance control group. Through such division, the battery pack under the real-time working condition mode is clearly divided into the passive balance control group and the active balance control group, laying a foundation for the subsequent load balance control operation.

[0037] During the vehicle operation process, multiple key parameters are collected and recorded in real time. First, it is the data related to the battery pack, including the voltage, current, temperature, and state of charge (SOC) of the battery pack, etc. These data can reflect the working state of the battery pack under the current real-time working condition. At the same time, the operation parameters of the vehicle also need to be recorded, such as vehicle speed, acceleration, the inclination angle of the vehicle (if there is climbing or special road conditions), and the state of the brake pedal (if in the braking condition), etc. Through the real-time collection and integration of these multi-dimensional data, a complete real-time operation working condition data set is formed. This data set will provide important data support for the subsequent balance identification and load balance control, so as to better understand the actual operation situation of the vehicle under the current real-time working condition, and thus make more accurate control decisions.

[0038] For the passive balance control group, a passive balance module including a power-consuming element (such as a variable resistor) and a MOSFET is configured. Here, a simple algorithm based on the load difference ratio is used to determine the passive balance control parameters. First, the degree of load difference between battery packs in the group is calculated by comparing parameters such as the current, voltage, and SOC of the battery packs. For example, if the current of a battery pack is significantly greater than that of other battery packs, it indicates a large load difference. Then, according to the preset proportional relationship, the degree of load difference is converted into the resistance rate and resistance duration of the variable resistor. For example, if the load difference is large, a faster resistance rate and a longer resistance duration are required to consume the excess energy to achieve load balance. These calculated resistance rate and resistance duration are the passive balance control parameters. For the active balance control group, a bidirectional DC-DC converter is configured, and the same algorithm based on the load difference ratio is used to determine the active balance control parameters. First, the degree of load difference between battery packs in the group is determined by comparing parameters such as the current, voltage, and SOC of the battery packs. Then, based on the degree of load difference and the working principle of the bidirectional DC-DC converter, the energy transfer direction (i.e., transferring energy from the battery pack with a high load to the battery pack with a low load), the energy transfer time (determining the time required to transfer energy according to the size of the load difference), and the energy transfer rate (determining the speed of energy transfer according to the size of the load difference and time) are calculated. These calculated parameters are the active balance control parameters. Finally, the passive balance control parameters and the active balance control parameters are used to perform load balance control on the passive balance control group and the active balance control group respectively. For the passive balance control group, the load of the battery pack is balanced by adjusting the resistance rate and resistance duration of the variable resistor. For the active balance control group, the working state of the bidirectional DC-DC converter is adjusted according to the energy transfer direction, energy transfer time, and energy transfer rate to achieve the balance of the battery pack load. In this way, under different real-time working conditions, the load balance control of the battery pack can be effectively performed, improving the overall performance and service life of the battery pack of new energy vehicles.

[0039] In a possible implementation manner, step S540 further includes: Step S541: Configure a passive balance module for the first type of battery pack, and connect the balance control grouping model to the passive balance module, where the passive balance module includes a power-consuming element and a MOSFET, the power-consuming element is a variable resistor, and the MOSFET is used to turn on the variable resistor.

[0040] Step S542: Obtain the passive balance control parameters of the power-consuming element based on the proportional control algorithm of the load difference, including the resistance rate and the resistance duration.

[0041] Specifically, for the first type of battery pack, a passive balancing module is configured. Since the load indicators of the first type of battery pack are within a specific range and require a relatively simple balancing control method, a passive balancing module is configured. The passive balancing module mainly consists of two parts, namely an energy-consuming element and a MOSFET. Among them, the energy-consuming element is a variable resistor. During the entire passive balancing control process, the variable resistor adjusts the load balance of the battery pack by consuming electrical energy according to different working conditions and control requirements. Its resistance value can be dynamically adjusted according to the actual situation to achieve adaptation to different load differences. The MOSFET is used to conduct the variable resistor. The MOSFET is a metal-oxide-semiconductor field-effect transistor, which has good switching characteristics and control performance. In the passive balancing module, the MOSFET receives the control signal from the balancing control packet model and decides whether to conduct the variable resistor according to the requirements of the signal. When the MOSFET conducts the variable resistor, the circuit forms a path, and the variable resistor starts to consume electrical energy, thereby adjusting the load of the battery pack. In addition, a connection is established between the passive balancing module and the balancing control packet model, and this connection is the key to achieving precise control. Through this connection, the balancing control packet model can obtain various information about the battery pack in real time, such as load indicators, voltage, current, etc., and generate corresponding control signals according to this information and send them to the MOSFET. In this way, according to the actual load situation of the battery pack, the working state of the variable resistor can be controlled, and thus the load balancing control of the first type of battery pack can be effectively achieved.

[0042] Determine the load difference between battery packs and analyze the relevant parameters in the real-time operating condition dataset. For example, by comparing parameters such as the current, voltage, and state of charge (SOC) of the battery packs to quantify the load difference. If the current difference between two battery packs is large, it indicates that there is an obvious difference in their loads; at the same time, differences in voltage and SOC will also affect the load difference. Considering these factors comprehensively, calculate a value that can accurately reflect the degree of load difference. Then, based on this degree of load difference, use the proportional control algorithm to determine the resistance rate and resistance duration. For the resistance rate, when the degree of load difference is large, in order to balance the load faster, a higher resistance rate is required. A proportional relationship can be set. For example, the degree of load difference is proportional to the resistance rate, that is, the resistance rate increases as the degree of load difference increases. For example, the resistance rate R rate = k1×D, where k1 is a proportionality coefficient and D is the degree of load difference. For the resistance duration, it is also determined based on the degree of load difference. When the load difference is large, a longer resistance duration is required to ensure sufficient energy consumption to achieve load balance. A similar proportional relationship is set, such as the resistance duration R time= k2×D + C, where k2 is a proportionality coefficient and C is a constant term used to account for some basic time requirements (it may take a certain amount of time to adjust even when the load difference is small). Through such a proportional control algorithm based on load difference, the passive balance control parameters of the energy-consuming component, namely the resistance rate and resistance duration, can be accurately obtained, thus providing effective parameter support for the subsequent load balance control of the first type of battery pack.

[0043] In a possible implementation manner, step S540 further includes: Step S543: Configure an active balance module for the second type of battery pack. The balance control grouping model is connected to the active balance module, where the active balance module includes a bidirectional DC-DC converter.

[0044] Step S544: Obtain the active balance control parameters of the bidirectional DC-DC converter based on the proportional control algorithm of load difference, including the energy transfer direction, energy transfer time, and energy transfer rate.

[0045] Specifically, for the second type of battery pack, due to the particularity of its load indicators, it is necessary to configure an active balance module to achieve more precise load balance control. The active balance module includes a bidirectional DC-DC converter. The bidirectional DC-DC converter is a device that can perform DC voltage conversion in two directions and plays a key role in active balance control. The active balance module is connected to the balance control grouping model. This connection enables the balance control grouping model to send control instructions to the active balance module according to the battery pack load situation reflected by the real-time operation condition data set, thereby achieving effective control of the bidirectional DC-DC converter.

[0046] The proportional control algorithm based on load difference is used to obtain the active balance control parameters of the bidirectional DC-DC converter. First, determine the load difference between battery packs. By analyzing parameters such as the voltage, current, and state of charge (SOC) of the battery packs in the real-time operating condition dataset, calculate the degree of load difference between the battery packs. For example, if the current difference between two battery packs is large, and there are also obvious differences in voltage and SOC, then it is determined that there is a large load difference between them. Then, determine the energy transfer direction according to the degree of load difference. When the load of one battery pack is higher than that of another battery pack, the energy transfer direction is from the battery pack with a high load to the battery pack with a low load to achieve load balance. Next, determine the energy transfer time. The energy transfer time is related to the degree of load difference. The greater the load difference, the more energy needs to be transferred, and the longer the energy transfer time. Determine the energy transfer time according to a pre-set functional relationship. The energy transfer time T = k1×D + c1, where k1 is the proportionality coefficient, D is the degree of load difference, and c1 is a constant term used to consider some basic time requirements. Finally, determine the energy transfer rate. The energy transfer rate is also related to the degree of load difference. The greater the load difference, the higher the energy transfer rate is required to achieve load balance faster. Determine the energy transfer rate according to a pre-set functional relationship. The energy transfer rate R = k2×D + c2, where k2 is the proportionality coefficient, D is the degree of load difference, and c2 is a constant term used to consider some basic configuration and limiting conditions. Through such a proportional control algorithm based on load difference, accurately obtain the active balance control parameters of the bidirectional DC-DC converter, including the energy transfer direction, energy transfer time, and energy transfer rate, so as to achieve the load balance control of the second type of battery pack.

[0047] In a possible implementation manner, step S500 further includes: Step S550: Obtain the mode stability according to the mode switching frequency and the mode duration.

[0048] Step S560: When the mode stability is greater than the preset mode stability, activate the balance control grouping model.

[0049] Specifically, evaluate the mode stability based on the mode switching frequency and the mode duration of the new energy vehicle. The mode switching frequency refers to the frequency at which the vehicle switches between different working condition modes. If the vehicle switches frequently between working condition modes such as acceleration, deceleration, climbing, and braking in a short period of time, then the mode switching frequency is high. For example, in urban traffic, the vehicle may continuously switch between acceleration and deceleration modes due to frequent traffic lights and traffic congestion. The mode duration refers to the length of time the vehicle continuously operates in each working condition mode. If the vehicle can continuously operate in a certain working condition mode for a long time, it means that the duration of this mode is long.

[0050] By comprehensively considering the mode switching frequency and the mode duration, the stability of the current vehicle operating state is accurately evaluated. If the mode switching frequency is low and the mode duration is long, then it can be considered that the vehicle's operating mode is relatively stable; conversely, if the mode switching frequency is high and the mode duration is short, then the vehicle's operating mode is relatively unstable. A calculation formula is set to quantify the mode stability, and the mode stability = mode duration / mode switching frequency is used to achieve this.

[0051] When the mode stability calculated through step S510 is greater than the preset mode stability, the balance control grouping model is activated. The preset mode stability is a threshold set in advance, which represents the standard for starting the balance control grouping model when the vehicle operating mode is stable to a certain extent. Activating the balance control grouping model means starting the load balancing control of the battery pack of the new energy vehicle. The model will divide the battery pack into a passive balance control group or an active balance control group according to the current real-time working condition mode, and perform load balancing control according to the corresponding strategy. For example, if the vehicle is in a relatively stable acceleration working condition mode, the model will divide the eligible battery packs into the corresponding control groups and adjust the load balance of the battery packs according to the preset control parameters to ensure the performance and life of the battery packs. Such an operation can timely and effectively perform load balancing control on the battery pack when the vehicle operating mode is relatively stable, improving the overall performance and reliability of the new energy vehicle.

[0052] In a possible implementation manner, step S100 further includes: Step S110: Obtain the battery pack distribution position information of the N battery packs.

[0053] Step S120: Identify the heat source area according to the battery pack distribution position information, obtain the identification battery packs, divide the identification battery packs into the active balance control group, and update the balance control grouping strategy under each characteristic working condition mode.

[0054] Specifically, the distribution position information of N battery packs is obtained through design drawings and markings. In the design stage of new energy vehicles, detailed vehicle structure design drawings will be drawn, and the installation positions of each battery pack in the vehicle are clearly marked on these drawings. The drawings will show the specific coordinate positions of the battery packs on the vehicle chassis or indicate that they are located in specific carriage areas. During the vehicle manufacturing process, according to the markings on the design drawings, the battery packs are accurately installed in the designated positions. At the same time, in order to ensure the accuracy of the installation, multiple inspections and calibrations are also carried out during the manufacturing process to ensure that the distribution positions of the battery packs are exactly the same as those on the design drawings. Obtaining the battery pack distribution position information through design drawings and markings has high accuracy and reliability. The design drawings are carefully designed and verified and can provide detailed and accurate position information.

[0055] Identify the heat source areas based on the obtained distribution position information of the battery packs. In new energy vehicles, the heat source areas are the parts that generate a large amount of heat during vehicle operation. For example, the motor generates a relatively high amount of heat during operation and becomes a major heat source; electronic devices also dissipate heat during operation; the braking system also generates a certain amount of heat during frequent braking. By analyzing the relationship between the distribution position of the battery packs and these potential heat source areas, determine which battery packs are near the heat sources. If the distance between a certain battery pack and the heat source is within a certain range, it can be marked as a marked battery pack. For example, if a battery pack is very close to the motor or located in an area where electronic devices are concentrated, then this battery pack is likely to be affected by the heat source. The marked battery packs are classified into the active balance control group because these battery packs require a more aggressive load balancing control strategy. The battery packs near the heat sources will affect their performance and lifespan due to the temperature rise. The active balance control group usually uses a bidirectional DC-DC converter to more precisely adjust the load of the battery packs to cope with the possible temperature rise and performance changes. By classifying the marked battery packs into the active balance control group, more targeted control is carried out on these battery packs to ensure that they can maintain good performance in the heat source environment. Finally, update the balance control grouping strategy for each characteristic working condition mode. Since the marked battery packs are classified into the active balance control group, the original balance control grouping strategy needs to be adjusted accordingly, including re-determining the classification criteria for the active balance control group and the passive balance control group under different working condition modes, adjusting control parameters, etc. For example, in the acceleration working condition mode, the original strategy was to classify the battery packs into different control groups according to the load index. Now, the existence of the marked battery packs needs to be considered and the strategy optimized to ensure that the battery packs can be effectively load-balanced under various working conditions, so that the balance control grouping strategy is more adaptable to the actual situation and improves the overall performance and reliability of the battery packs of new energy vehicles.

[0056] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. In addition, the specific embodiments of this specification have been described. Moreover, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0057] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0058] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A dynamic balance control method for a new energy vehicle battery pack, characterized in that, The method includes: Obtaining N battery packs of a new energy vehicle; Setting multiple characteristic working condition modes of the new energy vehicle, where the multiple characteristic working condition modes include an acceleration working condition mode, a deceleration working condition mode, a climbing working condition mode, and a braking working condition mode; Collecting a historical operating condition data set of the N battery packs under different characteristic working condition modes; Performing battery load calculation on the historical operating condition data set, outputting N load index samples corresponding to the N battery packs under different characteristic working condition modes, and constructing a balance control grouping model; Identifying the real-time working condition mode of the new energy vehicle, outputting the balance control battery packs divided under the real-time working condition mode based on the balance control grouping model, and performing load balance control on the balance control battery packs.

2. The method according to claim 1, wherein Constructing a balance control grouping model, the method includes: Analyzing the N load index samples corresponding to the N battery packs under each characteristic working condition mode, and identifying the first type of battery packs and the second type of battery packs under each characteristic working condition mode; Generating a passive balance control group with the first type of battery packs, generating an active balance control group with the second type of battery packs, and obtaining a balance control grouping strategy under each characteristic working condition mode; Constructing the balance control grouping model according to the balance control grouping strategy under each characteristic working condition mode.

3. The method according to claim 2, wherein The first type of battery packs are battery packs with load indexes greater than a first preset load index and less than a second preset load index, and the second type of battery packs are battery packs with load indexes less than or equal to the first preset load index and greater than or equal to the second preset load index, where the first preset load index is less than the second preset load index.

4. The method according to claim 2, wherein Outputting the balance control battery packs divided under the real-time working condition mode based on the balance control grouping model, the method includes: The balance control grouping model matches the real-time working condition mode with the multiple characteristic working condition modes, and obtains the balance control grouping strategy under the real-time working condition mode; Outputting the passive balance control group and the active balance control group divided under the real-time working condition mode according to the balance control grouping strategy; Recording the real-time operating condition data set of the new energy vehicle under the real-time working condition mode; Performing equilibrium identification according to the real-time operating condition data set, outputting passive balance control parameters and active balance control parameters, and performing load balance control on the passive balance control group and the active balance control group respectively with the passive balance control parameters and the active balance control parameters.

5. The method according to claim 4, characterized in that Performing equilibrium identification according to the real-time operating condition data set and outputting passive balance control parameters, the method includes: Configuring a passive balance module for the first type of battery packs, connecting the balance control grouping model to the passive balance module, where the passive balance module includes an energy-consuming element and a MOSFET, the energy-consuming element is a variable resistor, and the MOSFET is used to conduct the variable resistor; Obtaining the passive balance control parameters of the energy-consuming element based on a proportional control algorithm for load difference, including a resistance rate and a resistance duration.

6. The method according to claim 4, characterized in that, Performing equilibrium identification according to the real-time operating condition data set and outputting active balance control parameters, the method includes: An active balancing module is configured for the second type of battery pack, and the balance control grouping model is connected to the active balancing module, wherein the active balancing module includes a bidirectional DC-DC converter; Obtain the active balance control parameters of the bidirectional DC-DC converter based on a proportional control algorithm for load differences, including the energy transfer direction, energy transfer time, and energy transfer rate.

7. The method according to claim 1, wherein Identify the real-time operating condition mode of the new energy vehicle, and the method further includes: Perform data analysis on the real-time operating condition mode of the new energy vehicle to obtain the mode switching frequency and mode duration; Obtain the mode stability according to the mode switching frequency and mode duration; When the mode stability is greater than a preset mode stability, activate the balance control grouping model.

8. The method according to claim 2, wherein Obtain N battery packs of the new energy vehicle, and the method further includes: Obtain the battery pack distribution position information of the N battery packs; Identify the heat source area according to the battery pack distribution position information to obtain the identification battery pack, divide the identification battery pack into the active balance control group, and update the balance control grouping strategy under each characteristic operating condition mode.

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